A task processing method, apparatus, and system based on a distributed system is described. The method comprises: obtaining, by a main process of a task processing device, task information from a master control device by initiating a task request, where the task information corresponding to the task request is stored in the master control device. Further, in response to obtaining the task information, starting, by the main process, a corresponding task subprocess, in the task processing device, by triggering a proxy process, in the task processing device, for executing the task information.

A method, computer system, and computer program product for optimizing a number of robots for operation of a process at a target system. The method may include providing a plurality of available robots to carry out tasks in the process at the target system. The method may monitor the target system by carrying out the process or part of the process with a varying number of robots to determine the processor utilization whilst the robots are executing a varying number of tasks. The method may balance process constraints of the execution of the process with physical system constraints of the target system by measuring a relationship between a number of tasks at a transactional level and the processor utilization. The method may output the optimized number of robots to be allocated for the process or part of the process.

A method includes a second terminal that displays a first interface, and receives a first operation of a user on content on the first interface. The second terminal sends data to a first terminal in response to the first operation when an input source of the first operation is an input device of the first terminal, where the data is used by the first terminal to display a second interface on a display of the first terminal. The second terminal displays the second interface on a display of the second terminal in response to the first operation when the input source of the first operation is an input device of the second terminal.

Techniques disclosed herein relate to migrating virtual computing instances such as virtual machines (VMs). In one embodiment, VMs are migrated across different virtual infrastructure platforms by, among other things, translating between resource models used by virtual infrastructure managers (VIMs) that manage the different virtual infrastructure platforms. VM migrations may also be validated prior to being performed, including based on resource policies that define what is and/or is not allowed to migrate, thereby providing compliance and controls for borderless data centers. In addition, an agent-based technique may be used to migrate VMs and physical servers to virtual infrastructure, without requiring access to an underlying hypervisor layer.

Systems and method are provided for receiving, at a virtualized management operation engine (VMOE) of a distributed system, a management task request from a client for one or more services. The management task request may be to an orchestrator of a first application server of one or more application servers of the distributed system. The first application server having the orchestrator may determine whether the management task request is valid. The first application server having the orchestrator may generate a request message based on the valid management task request. A message bus may broadcast the generated request message to one or more agents of the one or more application servers of the distributed system. The one or more agents may execute an operation based on the request message when the request message is intended for the one or more agents.

A robot application is executed by executing a plurality of kinds of virtual containers in cooperation with each other. To this end, a robot application management device (100), at least one robot device (300) and at least one computer device (400) are connected to each other via a local area network (600). A group of devices including these devices are managed as a cluster for executing the robot application, and each virtual container is placed and activated in any of the group of devices composing the cluster.

Methods, systems, and computer-readable media for centralized management of remote endpoint devices are disclosed. Instances of agent software are installed on endpoint devices that are external to a multi-tenant provider network. The agent software is communicatively coupled to a centralized management service of the multi-tenant provider network. A software package is selected from a marketplace service of the multi-tenant provider network. The marketplace service comprises product offerings for a plurality of software packages. The centralized management service sends information indicative of a configuration associated with the software package to the agent software of one or more of the endpoint devices that are associated with the software package. The centralized management service receives, from the agent software, an indication that the configuration has been deployed on the one or more of the endpoint devices.

Methods, apparatus, systems and machine-readable storage media of an edge computing device which is enabled to access and select the use of local or remote acceleration resources for edge computing processing is disclosed. In an example, an edge computing device obtains first telemetry information that indicates availability of local acceleration circuitry to execute a function, and obtains second telemetry that indicates availability of a remote acceleration function to execute the function. An estimated time (and cost or other identifiable or estimateable considerations) to execute the function at the respective location is identified. The use of the local acceleration circuitry or the remote acceleration resource is selected based on the estimated time and other appropriate factors in relation to a service level agreement.

Embodiments relate to a system, a program product, and a method to support a migration operation. Risk factors associated with a migration operation are assessed. The assessment includes assigning respective risk score values to the identified risk factors, assigning respective weight values to the identified risk factors, and calculating a composite risk score based on the assigned risk score values and the assigned weight values. At least one remediation action is recommended to reduce risk to the migration operation. The at least one recommended remediation action is implemented prior to execution of the migration operation. A migration plan incorporating the remediation action for the migration operation is generated. At least one machine learning (ML) model is employed in connection with (a) the subjecting of the identified risk factors to the assessment and/or (b) the recommending of at least one remediation action to reduce risk to the migration operation.

Task delegation and cooperation for automated assistants is presented. A method comprises receiving, at a centralized support center that is in contact with a plurality of automated assistants including a first automated assistant and a second automated assistant, a request to perform a task on behalf of an individual, formulating, at the centralized support center, the task as a plurality of sub-tasks including a first sub-task and a second sub-task, delegating, at the centralized support center, the first sub-task to the first automated assistant, based on a determination at the centralized support center that the first automated assistant is capable of performing the first sub-task, and delegating, at the centralized support center, the second sub-task to the second automated assistant, based on a determination at the centralized support center that the second automated assistant is capable of performing the second sub-task.